Modelling Liquefied Petroleum Gas Prices in Nigeria Using Time Series Machine Learning Models

Abstract

Background: The usage of liquefied petroleum gas (LPG) in households has been increasing in recent years. The energy consumption by households is difficult to forecast due to the nature of the independent variables. Deep learning models has been broadly utilized in the machine learning area to model time series data, most notably in the area of forecasting. Objective: This study was to determine the best model for LPG price prediction in Nigeria. Methods: In this work, the neural network autoregressive (NNETAR) model, naive forecasting, and the autoregressive integrated moving average (ARIMA) models were used to model the price of LPG prices in 37 states (including the Federal Capital Territory) of Nigeria, with input variables in the form of the price of refilling LPG for 12.5kg from January 2016 to April 2019 covering a 1480 data points. The mean absolute percentage errors (MAPE) were used to evaluate the performance of the model. Results: The present study suggested that Adamawa has the lowest price of 12.5kg refilling LPG from January 2016 to April 2019 in the North East with a price of 2126.879 naira, FCT has the lowest price of 12.5kg refilling LPG from January 2016 to April 2019 in the North Central with a price of 2003.056 naira, Kaduna has the lowest price of 12.5kg refilling LPG from January 2016 to April 2019 in the North West with a price of 2006.436 naira, Edo has the lowest price of 12.5kg refilling LPG from January 2016 to April 2019 in the South South with a price of 2092.955 naira, Ebonyi has the lowest price of 12.5kg refilling LPG from January 2016 to April 2019 in the South East with a price of 2033.262 naira and Ekiti has the lowest price of 12.5kg refilling LPG from January 2016 to April 2019 in the South West with a price of 2008.860 naira. Conclusion: Naive produced lower MAPE for more states compared to NNETAR and ARIMA models.